Robust \(H_\infty\) control for nonlinear uncertain switched descriptor systems with time delay and nonlinear input: a sliding mode approach
type: Item
instance of: scholarly article
title: Robust \(H_\infty\) control for nonlinear uncertain switched descriptor systems with time delay and nonlinear input: a sliding mode approach
zbMATH Open document ID: 1407.93109
DOI: 10.1155/2017/1027909
author: Mourad Kchaou
author: Saleh Al Ahmadi
published in: Complexity
publication date: 2019-02-18T00:00:00Z
review text: Summary: This paper addresses the problem of sliding mode control (SMC) design for a class of uncertain switched descriptor systems with state delay and nonlinear input. An integral sliding function is designed and an adaptive sliding mode controller for the reaching motion is then synthesised such that the trajectories of the resulting closed-loop system can be driven onto a prescribed sliding surface and maintained there for all subsequent times. Moreover, based on a new Lyapunov-Krasovskii functional, a delay-dependent sufficient condition is established such that the admissibility as well as the \(H_\infty\) performance requirement of the sliding mode dynamics can be guaranteed in the presence of time delay, external disturbances, and nonlinear input which comprises dead-zones and/or sector nonlinearities. The major contributions of this paper of this approach include (i) the closed-loop system exhibiting strong robustness against nonlinear dynamics and (ii) the control scheme enjoying the chattering-free characteristic. Finally, two representative examples are given to illustrate the theoretical developments.
Mathematics Subject Classification ID: 93B36
Mathematics Subject Classification ID: 93B35
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93C41
zbMATH DE Number: 7024592
Wikidata QID: Q59142864
cites work: ImageJ
cites work: The EM/MPM algorithm for segmentation of textured images: Analysis and further experimentals results
cites work: Support-vector networks
cites work: Probabilistic Solution of Ill-Posed Problems in Computational Vision
cites work: Q5396710
cites work: A primal-dual perspective of online learning algorithms
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describes a project that uses: BSDS
describes a project that uses: iCoseg
describes a project that uses: GrabCut
describes a project that uses: Scikit
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describes a project that uses: Python
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/1027909
OpenAlex ID: W2625180053
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description: scientific article; zbMATH DE number 7024592